Examining Quantitative Metrics
Associate Professor in Data Science and Genetics at the University of East Anglia.
Academic background in Behavioural Ecology, Genetics, and Insect Pest Control.
I teach Genetics, Programming, and Statistics
A short talk on using quantitative data to measure student success
I hope you end up with more questions than answers after this talk!
69 students opted-in to measures of:
Coursework Days - Time between coursework started and submitted
Learning Hours - Time on system before coursework released
Coursework Hours - Time on system after coursework released
Attendance - In-person attendance
Weekly assignment - Completion of weekly tasks/tests
| Characteristic | Beta | 95% CI1 | p-value |
|---|---|---|---|
| (Intercept) | 66 | 63, 68 | <0.001 |
| Coursework Hours | 0.07 | -0.15, 0.30 | 0.5 |
| Coursework Days | 0.81 | 0.58, 1.0 | <0.001 |
| Attendance | -1.8 | -6.1, 2.4 | 0.4 |
| Coursework Hours:Attendance | 0.30 | 0.01, 0.60 | 0.045 |
| 1 CI = Confidence Interval | |||
Mean coursework mark 66%
Model accounts for 60% of variance in student attainment
Multiple candidate models were combined to produce an ensemble model that provides the most robust and reliable predictions
9/69 students did not submit coursework
Using a logistic regression model to separate coursework by submission/non-submission
Can we use quantitative measures to identify students at risk of non-submission?
The first four weeks are a good indicator for risk of non-submission
Black box - Machine learning
PROS
Maximum predictive ability
Identify and intervene with students
Can be trained on a wide range of data
CONS
Identify key variables
PROS
Transparent
Engages students with decision making
CONS
Less accurate
More difficult to implement?
Studies to compare Quantitative and Qualitative measures of student success
Evalutate student perceptions of requirements or indicators of “academic success”
Larger scale data training to build predictive models
Increasing amounts of data available on student participation and engagement
Student engagement is not as simple as “In-person attendance”
Quantitative markers CAN be used as predictors of academic achievement